Offer Matching for a User Segment

ABSTRACT

User data and a plurality of micro-segment definitions are received. Each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign. Further, a each micro-segment definition from the plurality of micro-segment definitions is parsed into a plurality of parsed expression segments that indicate a plurality of micro-segment condition rules. The plurality of parsed expression segments are compiled into an executable object that indicates a plurality of instructions to determine if the user data matches the plurality of micro-segment definitions. Each micro-segment definition is processed to apply the plurality of micro-segment condition rules to the user data to determine a match of a user belonging to a micro-segment. Further, a score is assigned to indicate the strength of each match. In addition, each match is ranked according to the score for each match.

RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 13/039,242, filed Mar. 2, 2011, entitled“Sequential Engine that Computes User and Offer Matching intoMicro-Segments,” the entire disclosure of which is hereby incorporatedby reference herein in its entirety.

BACKGROUND

1. Field

This disclosure generally relates to classification of consumers. Moreparticularly, the disclosure relates to determining micro-segments towhich consumers belong.

2. General Background

Content providers, merchants, and marketers have to precisely define andtarget highly specific market segments in order to effectively deliverthe most relevant online content. Examples of the most relevant onlinecontent are advertising, offers, entertainment, news, etc.

A micro-segment is a precise division of a market or population that istypically identified by marketers through advanced technology andtechniques. For example, data mining, artificial intelligence, andvarious algorithms may be utilized. These technologies and techniquesare utilized to group consumers into fine-grained segments byrecognizing and predicting minute consumer spending and behavioralpatterns, i.e., micro-segmentation. In other words, a micro-segment is agroup of (purchase) decision makers who share similar attributes,purchase behavior, and/or level of interest in a specific set offeatures. In the current environment, however, classifying andsegmenting a new user community into micro-segments may be difficult fora number of reasons. In particular, consumers are increasingly filteringcontent and marketing messages, which reduces marketer efficacy.Further, even as more consumer data and behaviors are collected, mostare under-utilized because of the lack of industry expertise andlimitations of available technology. In addition, meaningfulsegmentation within newly created user communities and populations isdifficult.

Further, segmentation difficulties also affect numerous websites thatleverage the recorded behaviors of large numbers of site users indetermining recommended content, products, and services for various usersegments. Recommendation systems utilize algorithms that may vary fromk-nearest neighbor approaches to preference/interest/taste similaritymethods, e.g., found by using Pearson Correlation, to collaborativefiltering algorithms, e.g., people who buy X also buy Y. A challengewith all of these approaches is having an accurate segmentation of verylarge user populations based on recorded preferences and behaviorsbefore the system can make recommendations.

SUMMARY

In one aspect of the disclosure, a computer program product is provided.The computer program product includes a computer useable medium having acomputer readable program. The computer readable program when executedon a computer causes the computer to receive user data and a pluralityof micro-segment definitions such that each micro-segment definition inthe plurality of micro-segment definitions corresponds to one or moreoffers in an offer provider campaign. Further, the computer readableprogram when executed on the computer causes the computer to parse, witha micro-segment parser, each micro-segment definition from the pluralityof micro-segment definitions into a plurality of parsed expressionsegments that indicate a plurality of micro-segment condition rules. Inaddition, the computer readable program when executed on the computercauses the computer to compile, with a compiler, the plurality of parsedexpression segments into an executable object that indicates a pluralityof instructions to determine if the user data matches the plurality ofmicro-segment definitions. The computer readable program when executedon the computer also causes the computer to serially process eachmicro-segment definition, with a sequential evaluation engine, to applythe plurality of micro-segment condition rules to the user data todetermine a match of a user belonging to a micro-segment. Further, thecomputer readable program when executed on the computer causes thecomputer to assign, with the sequential evaluation engine, a score toindicate the strength of each match. In addition, the computer readableprogram when executed on the computer causes the computer to rank, withthe sequential evaluation engine, each match according to the score foreach match.

In another aspect of the disclosure, a process is provided. The processreceives user data and a plurality of micro-segment definitions suchthat each micro-segment definition in the plurality of micro-segmentdefinitions corresponds to one or more offers in an offer providercampaign. Further, the process parses, with a micro-segment parser, eachmicro-segment definition from the plurality of micro-segment definitionsinto a plurality of parsed expression segments that indicate a pluralityof micro-segment condition rules. In addition, the process compiles,with a compiler, the plurality of parsed expression segments into anexecutable object that indicates a plurality of instructions todetermine if the user data matches the plurality of micro-segmentdefinitions. The process also serially processes each micro-segmentdefinition, with a sequential evaluation engine, to apply the pluralityof micro-segment condition rules to the user data to determine a matchof a user belonging to a micro-segment. Further, the process assigns,with the sequential evaluation engine, a score to indicate the strengthof each match. In addition, the process ranks, with the sequentialevaluation engine, each match according to the score for each match.

In yet another aspect of the disclosure, a system is provided. Thesystem includes a reception module that receives user data and aplurality of micro-segment definitions such that each micro-segmentdefinition in the plurality of micro-segment definitions corresponds toone or more offers in an offer provider campaign. Further, the systemincludes a micro-segment parser that parses each micro-segmentdefinition from the plurality of micro-segment definitions into aplurality of parsed expression segments that indicate a plurality ofmicro-segment condition rules. In addition, the system includes acompiler that compiles the plurality of parsed expression segments intoan executable object that indicates a plurality of instructions todetermine if the user data matches the plurality of micro-segmentdefinitions. The system also includes a sequential evaluation enginethat (i) serially processes each micro-segment definition to apply theplurality of micro-segment condition rules to the user data to determinea match of a user belonging to a micro-segment, (ii) assigns a score toindicate the strength of each match, and (iii) ranks each matchaccording to the score for each match.

DRAWINGS

The above-mentioned features of the present disclosure will become moreapparent with reference to the following description taken inconjunction with the accompanying drawings wherein like referencenumerals denote like elements and in which:

FIG. 1 illustrates a micro-segmentation system configuration.

FIG. 2 illustrates a micro-segment definition and structure.

FIG. 3 illustrates an example of micro-segment definition code.

FIG. 4 illustrates an example of a graphical user interface (“GUI”) thatmay be utilized to one more segments.

FIG. 5A illustrates an example of code 500 for the written form of theexpression.

FIG. 5B illustrates an example of a segment GUI in which selections aremade such that code generates segment definitions.

FIG. 6 illustrates an expression tree.

FIG. 7 illustrates an example of code that may be utilized for theexpression tree illustrated in FIG. 6.

FIG. 8 illustrates a process that is utilized for defining amicro-segment object.

FIG. 9 illustrates a system configuration that may be utilized fordefining a micro-segment.

FIG. 10 illustrates a process that computes user and offer matching intomicro-segments.

FIG. 11 illustrates a sequential engine matching system.

FIG. 12 illustrates a table that indicates a variety of segmentattributes.

FIG. 13A illustrates a row storage data layout for the segmentattributes illustrated in FIG. 12.

FIG. 13B illustrates a column storage data layout for the segmentattributes illustrated in FIG. 12.

FIG. 14 illustrates an analytics data storage system.

FIG. 15 illustrates a system configuration that may be utilized forcomputing user and offer matching into a micro-segment.

DETAILED DESCRIPTION

A sequential engine determines which micro-segments users belong to. Inone embodiment, the sequential engine has a single computation processor thread, e.g., a single core and linear sequential processing program.The sequential engine utilizes a definition of the micro-segment anduser data to compute a Boolean expression of True or False to determinewhether a predicate of the micro-segment is met. Further, non-Booleanpredicates may also be utilized, which result in real value results. Thesegment description is parsed into a segment definition parse tree. Thesegment definition parse tree is then compiled into bytecode. Thebytecode is then executed to return segment assignments and scores.

In one embodiment, demographic attributes and behavioral interests of auser are generalized. A segment has a collection of attribute comparisonpredicates, e.g., Gender==Male, connected by Boolean AND/OR operators,e.g., (Gender==Male) AND (Age between (20,30)). Accordingly, if aquantity n total data attributes are available, then the number ofmicro-segments may equal 2.sup.n−1, i.e., the total combination of nattributes. The micro-segment objects allow marketers to definemicro-segments from this large space of attributes. Those generalizeddemographic attributes and behavioral interest are then encapsulatedinto an object. The behavioral interests may include both positive andnegative interests, which allows creation of a comprehensive affinitymodel. An example of positive or negative interests is likes or dislikesof a brand. In addition, the object supports any complex attribute orinterest rule structure that is represented as a syntactic expressiontree. Attributes and predicate expressions for matching andrecommendations may then be developed for that syntactic expressionstree. Predicates can be connected through conjunctive operators and/ordisjunctive operators to create arbitrarily complex micro-segmentexpressions.

The micro-segment object is portable. In other words, the micro-segmentis not hardwired to a particular marketing campaign. The micro-segmentis reusable. Accordingly, the micro-segment object provides portableanalytics without specific details. In one embodiment, the micro-segmentis an opaque encoding of a set of attributes common to a population ofconsumers. For example, a micro-segment including males ages twenty-fivewith income between eighty thousand dollars to one hundred thousanddollars, and with an interest in sports cars may be encoded as SEG-XYZ.New consumers assigned into segment SEG-XYZ will by definition have thesame listed attributes. Given a larger population of consumers withother micro-segments, marketers that share micro-segment definitions anddata may perform analytics utilizing the SEG-XYZ encoding withoutrevealing to others the actual definition of the micro-segment. Amicro-segment definition is portable as a micro-segment that wassuccessful for one marketer may be shared and utilized by anothermarketer. The analytics themselves may be performed in a private mannerin which the attributes are not revealed to other parties. Thisconfiguration is useful in cases when marketers have made prioragreements to share segment definitions and micro-segment consumer data.

Numerous high-value micro-segments within newly created user communitiesmay be identified and created. Advertisers and marketers can automatethe creation of customized micro-segments to which they can deliverhighly targeted and relevant content across a range of multimediadevices. After the micro-segments are identified, they can be utilizedto automate the delivery of content, personalized directmicro-marketing, and micro-promotion campaigns, which target and appealto the specified tastes, needs, wants, and desires of the memberindividuals. Micro-marketing is the process by which the system modelseach consumer as having different ideas and feelings about a company'sproducts, services, prices, and promotions, and appeals to them in anappropriate manner. A consumer refers to a user who is a consumer andutilizes the configurations provided for herein. The micro-segmentsprovide a finer level of granularity than segments. Accordingly, themicro-segments may assist marketers in recognizing and predicting minuteconsumer spending and behavioral patterns. For example, themicro-segments may be utilized to leverage data sources such as coredemographics, category spending over time, fine-grained purchasehistory, and buying intent. Some of these data sources such as purchasehistory and category spending may be validated as they are coming fromthird parties, e.g., credit card companies. As a result, marketers areable to provide more accurate, precise, and targeted offers.

Further, membership within micro-segments may be incrementally andcontinuously updated within micro-segments. In addition, intentionalsemantics may be automatically detected and inferred utilizingadditionally analytics. For example, if a consumer belongs to a high-endcar interest segment, a high-end camera interest segment, and a high-endwatch segment, the system may infer that if the consumer has expressed ageneric interest in shirts, the consumer may additionally fit into ahigh-end shirt interest segment.

Further, recommendations may be quickly and accurately generatedregarding content, products and services to users within eachmicro-segment. A recommendation system may be utilized to perform therecommendations. The recommendation system is a system that employsinformation clustering and filtering techniques that attempt torecommend information content or product items that are likely to be ofinterest to a specific user (consumer) based on the cluster or segmenthe or she is in. In one embodiment, a recommendation system compares auser's behaviors and/or explicit profile to some referencecharacteristics and then seeks to predict the interest ‘rating’ that auser would give to an item they may have not yet considered. Thesecharacteristics may be from the information or product item (using acontent-based and/or attribute approach) or the user's socialenvironment (using collaborative filtering approaches).

In one embodiment, each micro-segment includes a specific set of keydiscriminating features (“KDFs”) that defines a group of attributesutilized by decision makers and a volume or value figure to indicate themicro-segment size. FIG. 1 illustrates a micro-segmentation systemconfiguration 100. The micro-segmentation system configuration 100 has amicro-segmentation system 102 that is a third-party trusted systembetween a merchant 104 and each of a plurality of users 106. The offerprovider 104 may be a company selling a product, a company selling aservice, a marketing company, an advertising company, or the like thatprovides a campaign to the micro-segmentation system. The campaignindicates a set of target attributes that the offer provider is lookingfor in marketing to particular users for a product or service. Thecampaign may include one or more offers. Accordingly, the set of targetattributes refers to the set of attributes the campaign is targeting. Asan example, the campaign may be an offer for sale of men's sneakers inthe United States of America. The micro-segmentation system 102 receivesthat campaign and also receives user attributes from the plurality ofusers 106. The attributes are properties or characteristics. An exampleof an attribute is gender. Accordingly, the values for the genderattribute may be male or female. The micro-segmentation system 102 thenperforms a determination of which users in the plurality of users 106have user attribute values that match the target attributes of thecampaign. In other words, the micro-segmentation system 102 evaluatesthe created micro-segment definitions, attributes values, and valuedistributions to determine the selectivity of the specificmicro-segment. The micro-segmentation system 102 determines amicro-segment 108 that includes users that match the target attributesof the campaign. In one embodiment, all of the target attributes have toequal the user attributes in order for the user to be placed into themicro-segment 108. In another embodiment, a minimum matching score hasto be met for the user to be placed into the micro-segment 108. As anexample, a user may not have to match all of the attributes, but maymatch enough of the attributes to generate a score that exceeds theoffer provider's minimum threshold and places the consumer in tomicro-segment 108. In another embodiment, a weighting mechanism isutilized to weigh certain attributes as opposed to other attributes inthe scoring methodology. For example, an age attribute may have a higherweighting in the scoring calculation than a geographic attribute. In oneembodiment, the system compensates for attribute bias to preventattribute overweighting. Similarly, marketers may be allowed tocustomize the weightings of micro-segment attributes in determining theselectivity of the micro-segment relative to candidate users.

In one embodiment, after the micro-segmentation system 102 automaticallyclassifies users into the micro-segment 108, the micro-segmentationsystem 102 sends a micro-segment data definition to the offer provider104. In one embodiment, the micro-segmentation system 102 capturesdefault definitions and/or training data for classifying existing and/ornew users. The quantity of segment definitions may range anywhere from afew to billions based upon the number of ways user attributes arecombined and utilized. In another embodiment, that micro-segment datadefinition does not include personal identity information of the usersin the micro-segment. In other words, the plurality of users provideattribute information to the micro-segmentation system 102 on a trustedbasis such that the micro-segmentation system does not send informationthat personally identifies the users to the offer provider 104. Thesystem may not send any data to the offer provider other thanrepresentative statistics or general statistics about the micro-segmentthey defined. As an example, a micro-segment may contain twenty-seventhousand three hundred thirty-two consumers. After the offer has beendelivered, seventeen thousand three hundred forty-four consumers lookedat the offer, three thousand four hundred forty-four consumers clickedon the offer to learn more, and six hundred thirty-four consumerspurchased the offer. Further, in one embodiment, the plurality of users106 provides permission to the micro-segmentation system 102 to sendthem offers. The micro-segment data definition received by the offerprovider 104 provides information such as the number of users in themicro-segment, their attribute values, etc. The offer provider 104 canquickly determine potential interest in a campaign among a targetaudience, without wasting advertising and resources on people who haveno interest in receiving advertising for this specific campaign. As aresult, the offer provider 104 can realistically determine if thecampaign is economically feasible and the amount of resources thatshould be dedicated to the campaign, etc. The offer provider can thensend an offer to the micro-segmentation system 102 based on themicro-segment data. In other words, the offer provider 104 is notsending the offer directly to the micro-segment 108. After receiving theoffer, the micro-segmentation system may then send the offer to themicro-segment. If users in the micro-segment would like to learn moreabout the offer or accept the offer, the users may then individuallycontact the offer provider by following a link or some other responsemechanism provided in the offer. In another embodiment, micro-segmentdata other than the micro-segment data definition may also be sent tothe offer provider 104. As an example, campaign performance statisticsmay be sent to the offer provider after the delivery of the campaign inaddition to the micro-segment data definition.

In one embodiment, the micro-segmentation system 102 also performsrecommendations. The micro-segmentation system 102 may deliver arecommendation to the user. In one embodiment, given any user, themicro-segmentation system 102 quickly locates all assignedmicro-segments and then utilizes the assigned micro-segments to locateproduct, service, and/or content offers based on the matchingmicro-segments to generate specific recommendations. Further, themicro-segmentation system 102 may store data regarding therecommendations upon which the user acts.

In one embodiment, before each user is classified, that user is scoredagainst all relevant micro-segments to determine the most probableclassifications. Further, micro-segment classifications may beefficiently assigned to users and searchable in real-time.

FIG. 2 illustrates a micro-segment definition and structure 200. Themicro-segment definition and structure 200 has a micro-segment object206 that may receive a campaign offer such as the first campaign offer202 and/or the second campaign offer 204. For illustrative purposes, themicro-segment object 206 receives the first campaign offer 202. As anexample, the micro-segment object 200 may receive a party_segmentidentifier that identifies a target party of the first campaign offer202. For instance, the party_segment identifier may be “Teenybopper.”The micro-segment object may also have segment metadata 208 thatincludes metadata about the segment. For example, the segment metadata208 may have an owner name, audience categories, description of thesegment, etc. The micro-segment object 206 may also have one or moresegment definitions. For example, the micro-segment object 206 may havea gender segment attribute definition 212 and an age segment attributedefinition 216. The micro-segment object 206 may also have segmentattribute value definitions for the respective segment attributedefinitions. For example, a gender segment attribute value definition210 may equal female and an age segment attribute value definition mayequal an age between nine and fourteen. Various distributions such adiscrete distribution, a range distribution, or a value distributionsuch as Cumulative Distribution Function (“CDF”) may be utilized.

FIG. 3 illustrates an example of micro-segment definition code 300. Aparty_segment name portion may provide the party_segment name. As anexample, the party_segment name may be “TeenyBopper.” Further, a segmentattribute data source definition may define the segment attribute datasources, e.g., gender. Further, a marketer readable segment descriptionmay provide a marketing description, e.g., “All Incomes, Women, Ages: 9to 14.” Further, segment attribute value definitions may be provided.

FIG. 4 illustrates an example of a GUI 400 that may be utilized todefine one more segments. As an example, a marketer may select one ormore segments to assign to an offer within a campaign from the GUI 400.The GUI 400 has a plurality of segments that may be selected by aninput. The segments may each have a segment name, code, gender, age,income, and/or other attributes. Further, the GUI 400 may allow the userto sort by combination of segment gender, age, income, and/or otherattributes. Further, minimum, maximum, and/or average values may beutilized.

All of the conditions in this text description are then expressed as acollection of CONDITIONS or predicates connected by Boolean AND and ORoperations. FIG. 5A illustrates an example of code 500 for the writtenform of the expression. In one embodiment, this segment expression canbe provided directly to an evaluation and execution engine forevaluation. Further, FIG. 5B illustrates an example of a segment GUI 550in which selections are made such that code 575 generates segmentdefinitions.

In another embodiment, an expression parse tree based on expressionsyntax rules is created and provided to the evaluation and executionengine. By utilizing an expression parse tree representation, a parsestep is eliminated on each expression match resulting in significantlyfaster execution of the segment matching expression.

[0043] FIG. 6 illustrates an expression tree 600. The expression tree600 is a tree of nodes that is created by a GUI tool. For example, amarketer who is creating segment definitions may utilize the GUI tool.Simple conditions or predicates specify a consumer attribute, e.g.,zipcode, a value or list of values, e.g., 94301, 94302, . . . , and acomparison or set operator, e.g., “in.” These individual conditions areevaluated, and the result of each condition is then utilized to satisfyone or more BOOLEAN expressions formed using a combination of AND or ORoperators. The micro-segment object utilizes a formal expression syntax,which describes all segment expressions that can be formed andrepresented. The following is an example segment definition that isprovided for a manufacturer interested in targeting consumers who livein certain cities, are a specific gender, have a specified income range,have made previous purchases in certain product categories, etc.: [0044]Consumer lives in Palo Alto, Sunnyvale, Santa Clara, or San Jose (basedon Zipcode) [0045] AND (either of:) [0046] Consumer is a Male and [0047]Consumer's Income is between $50K and $100K and [0048] Consumer'sDataProvider1MonthlyPowerBill>=$200 and [0049] OR [0050] Consumer'sproducts of interest are in “Green Electronics” or “Power Conservation”category or [0051] Consumer has purchased products from brands [0052]“Brand ABC” or “Brand XYZ”

The expression tree 600 has a first root AND node 602, a first OR leafnode 604, and a second OR leaf node 606. The first OR leaf node 604evaluates to TRUE if any of a first zipcode condition 608, a secondzipcode condition 610, a third zipcode condition 612, or a fourthzipcode condition 614 is met. Further, the second OR leaf node 606evaluates to true if a categories purchased condition 616 is met, abrands purchased condition 618 is met, or if an AND node 626 evaluatesto TRUE. The AND node 626 evaluates to TRUE if a gender condition 620 ismet, an income condition 622 is met, and a monthly power bill conditionis met. The results of the first OR leaf node 604 and the second OR leafnode 606 are utilized to evaluate the AND node 602. Both results have tobe TRUE for the AND node 602 to evaluate to TRUE. In other words, afterall simple condition nodes are evaluated to either TRUE or FALSE and allimmediate Boolean nodes are evaluated, parent Boolean nodes areevaluated by a recursive process until the root node of the expressiontree is reached. At this stage, a final TRUE or FALSE value is returnedto the system to determine if the consumer should be assigned into themarketer's defined segment.

In the case when consumer attribute values are missing, conditionscannot be evaluated to be either TRUE or FALSE values and a third valueNULL is used. NULL values can subsequently participate in Booleanoperations by using a three-valued logic system.

The expression tree 600 is provided as an example of an expressiongraph. A variety of other types of acyclic graphs may be utilized. Anacyclic graph is a structure that is utilized to group the expressionpredicates. The nodes in the acyclic graph may include different syntaxelements that form predicates. The syntactic acyclic graph guaranteesthat the expression is a valid expression that may be executed and thatthere will not be any syntax errors.

FIG. 7 illustrates an example of code 700 that may be utilized for theexpression tree 600 illustrated in FIG. 6. The specific values,conditions, node, code, etc. provided for throughout are intended onlyas examples. When the code 700 is parsed and compiled, the expressiontree 600 is produced.

FIG. 8 illustrates a process 800 that is utilized for defining amicro-segment object. At a process block 802, the process 800 receives,at a graphical user interface, a selection of one or more segmentattributes from an offer provider campaign. The one or more segmentattributes define one or more segments that correspond to one or moreoffers in the offer provider campaign. Further, at a process block 804,the process 800 generates a syntactic expression graph based on the oneor more segment attributes. In addition, at a process block 806, theprocess 800 generates a portable micro-segment object based on thesyntactic expression tree such that the portable micro-segment objectlacks dependence on the offer provider campaign.

FIG. 9 illustrates a system 900 that computes user and offer matchinginto micro-segments. The system 900 receives user data and a pluralityof micro-segment definitions. Each micro-segment definition in theplurality of micro-segment definitions corresponds to one or more offersin an offer provider campaign. As an example, one or more marketers mayenter the definitions of campaigns and one or more segment matchingexpressions and offers used by the campaign using a system GUI.Marketers may also choose to update or delete campaigns and segments.Further, the system 900 includes a micro-segment parser 902 that parseseach micro-segment definition from the plurality of micro-segmentdefinitions into a plurality of parsed expression segments that indicatea plurality of micro-segment condition rules. In one embodiment, theplurality of micro-segment definitions is received in a portablemicro-segment object that stores analytics without user identificationdata. In addition, the system 900 includes a compiler 904 that compilesthe plurality of parsed expression segments into an executable object,e.g., a bytecode object that indicates a plurality of instructions todetermine if the user data matches the plurality of micro-segmentdefinitions. In one embodiment, the instructions are high-levelinstructions. The system also includes a sequential evaluation engine906 that (i) serially processes each micro-segment definition to applythe plurality of micro-segment condition rules to the user data todetermine a match of a user belonging to a micro-segment, (ii) assigns ascore to indicate the strength of each match, and (iii) ranks each matchaccording to the score for each match. In one embodiment, the sequentialevaluation engine is stored on a client computing device. For example, auser may store the sequential evaluation engine on his or her owncomputing device. As a result, the user may effectively restrict userinformation from being transmitted to unintended entities. In anotherembodiment, the sequential evaluation engine is operated by a singlecore on a server. The output of the compiler may be stored into anon-SQL (“NoSQL”) database as well. In yet another embodiment, thesequential evaluation engine is operated by a single thread. The matchmay be determined according to a variety of logic systems. As anexample, the match may be determined according to three-valued logicsuch that one or more Boolean predicates and one or more non-Booleanpredicates are utilized. For instance, the Boolean values of True andFalse may be utilized along with the non-Boolean Value of Null. Further,the Null value may be a value between 0 and 1. As an example, 0.5 may bethe Null value. The score may equal a weighting coefficient multipliedby the Boolean or non-Boolean value. For example, a first attribute mayhave a higher weighting coefficient than a second attribute because thefirst attribute may be more important to the user and/or campaignprovider. Accordingly, an age attribute may have a weighting coefficientof 0.5. Therefore, the score may equal the value of Null multiplied bythe weighting coefficient, e.g., 0.5.times.0.5=0.25.

In another embodiment, a persist operation is performed such that eachexecutable object is named and stored in a database for later retrievaland use by the sequential evaluation engine 906. As an example, alogical but potentially physically distributed relational database,object-based or NOSQL key-value storage system, is utilized to storeexecutable “bytecode” objects for later use.

In yet another embodiment, a load/refresh operation is performed. Whenthe sequential evaluation engine 906 is invoked, or when new segmentdefinitions are created, or when existing segment definitions areupdated, the sequential evaluation engine 906 with the matching systemthen issues a request to load or refresh all needed segments. TheLoad/Refresh process may be full or incremental (using a differentialconfiguration if only a small number of segment definitions havechanged.).

In another embodiment, a match and offer delivery system utilizes thelist of generated segments that a user belongs to and identifiesrelevant product and service offers of interest to the user based onsegment criterion. A database is utilized to locate all offersassociated with specific segments; these offers are sent to the deliverysystem for presentation to the user.

FIG. 10 illustrates a process 1000 that computes user and offer matchinginto micro-segments. At a process block 1002, the process 1000 receivesuser data and a plurality of micro-segment definitions such that eachmicro-segment definition in the plurality of micro-segment definitionscorresponds to one or more offers in an offer provider campaign.Further, at a process block 1004, the process 1000 parses, with amicro-segment parser, each micro-segment definition from the pluralityof micro-segment definitions into a plurality of parsed expressionsegments that indicate a plurality of micro-segment condition rules. Inaddition, at a process block 1006, the process 1000 compiles, with acompiler, the plurality of parsed expression segments into an executableobject that indicates a plurality of instructions to determine if theuser data matches the plurality of micro-segment definitions. At aprocess block 1008, the process 1000 also serially processes eachmicro-segment definition, with a sequential evaluation engine, to applythe plurality of micro-segment condition rules to the user data todetermine a match of a user belonging to a micro-segment. Further, at aprocess block 1010, the process 1000 assigns, with the sequentialevaluation engine, a score to indicate the strength of each match. Inaddition, at a process block 1012, the process 1000 ranks, with thesequential evaluation engine, each match according to the score for eachmatch.

FIG. 11 illustrates a sequential engine matching system 1100. Sequentialengine source definitions 1102 receive campaigns 1104, a taxonomy 1106,and offers 1108 to generate segments 1110. Further, the sequentialengine source definitions generate data ontology attributes 1112 fromthe segments 1110. A sequential engine matcher 1114 has predicate rules1118 that receive the segments 1110 and attribute encoding/bindings 1120that receive the data ontology attributes 1112. An In-Memory Matcher1122 receives the attribute encoding/bindings 1120 and user data 1116.The In-Memory Matcher 1122 then generates user segments 1124. As anexample, a user segment may have a UserID, SegID, Score, and TimeStamp(“TS”). The user segments 1124 are then stored in an engine offersegment index 1126.

FIG. 12 illustrates a table 1200 that indicates a variety of segmentattributes. The table may include a variety of fields such as RecID,UID, Gender, Age, Income, and Kids. These fields are provided asexamples for illustrative purposes as a variety of other fields may beutilized.

FIG. 13A illustrates a row storage data layout 1300 for the segmentattributes illustrated in FIG. 12. The row storage data layout 1300provides a fast speed for getting/setting all attributes for user.

FIG. 13B illustrates a column storage data layout 1350 for the segmentattributes illustrated in FIG. 12. The column storage data layout 1350provides a fast speed for getting users with segment Attr=X value,segmenting users in batch mode into micro-segments, and counting usersin defined segments.

FIG. 14 illustrates an analytics data storage system 1400. In oneembodiment, a database is vertically partitioned by segment attribute.As an example, a first vertical partition is based on the attributevalue of gender whereas a second vertical partition is based on theattribute value of age.

FIG. 15 illustrates a system configuration 1500 that may be utilized forcomputing user and offer matching into a micro-segment. In oneembodiment, a micro-segment computational module 1502 interacts with amemory 1504. In one embodiment, the system configuration 1500 issuitable for storing and/or executing program code and is implementedusing a general purpose computer or any other hardware equivalents. Theprocessor 1506 is coupled, either directly or indirectly, to the memory1504 through a system bus. The memory 1504 can include local memoryemployed during actual execution of the program code, bulk storage,and/or cache memories which provide temporary storage of at least someprogram code in order to reduce the number of times code must beretrieved from bulk storage during execution.

The Input/Output (“I/O”) devices 1508 can be coupled directly to thesystem configuration 900 or through intervening input/outputcontrollers. Further, the I/O devices 1508 may include a keyboard, akeypad, a mouse, a microphone for capturing speech commands, a pointingdevice, and other user input devices that will be recognized by one ofordinary skill in the art. Further, the I/O devices 1508 may includeoutput devices such as a printer, display screen, or the like. Further,the I/O devices 1508 may include a receiver, transmitter, speaker,display, image capture sensor, biometric sensor, etc. In addition, theI/O devices 1508 may include storage devices such as a tape drive,floppy drive, hard disk drive, compact disk (“CD”) drive, etc. Any ofthe modules described herein may be single monolithic modules or moduleswith functionality distributed in a cloud computing infrastructureutilizing parallel and/or pipeline processing.

Network adapters may also be coupled to the system configuration 1500 toenable the system configuration 1500 to become coupled to other systems,remote printers, or storage devices through intervening private orpublic networks. Modems, cable modems, and Ethernet cards are just a fewof the currently available types of network adapters.

The processes described herein may be implemented in a general,multi-purpose or single purpose processor. Such a processor will executeinstructions, either at the assembly, compiled or machine-level, toperform the processes. Those instructions can be written by one ofordinary skill in the art following the description of the figurescorresponding to the processes and stored or transmitted on a computerreadable medium. The instructions may also be created using source codeor any other known computer-aided design tool. A computer readablemedium may be any medium capable of carrying those instructions andinclude a CD-ROM, DVD, magnetic or other optical disc, tape, siliconmemory (e.g., removable, non-removable, volatile or non-volatile),packetized or non-packetized data through wireline or wirelesstransmissions locally or remotely through a network. A computer isherein intended to include any device that has a general, multi-purposeor single purpose processor as described above.

It should be understood that the processes and systems described hereincan take the form of entirely hardware embodiments, entirely softwareembodiments, or embodiments containing both hardware and softwareelements. If software is utilized to implement the method or system, thesoftware can include but is not limited to firmware, resident software,microcode, etc.

It is understood that the processes and systems described herein mayalso be applied in other types of processes and systems. Those skilledin the art will appreciate that the various adaptations andmodifications of the embodiments of the processes and systems describedherein may be configured without departing from the scope and spirit ofthe present processes, systems, and computer program products.Therefore, it is to be understood that, within the scope of the appendedclaims, the present processes, systems, and computer program productsmay be practiced other than as specifically described herein.

What is claimed is:
 1. A system comprising: one or more processors; andat least one storage device storing a computer readable program, whereinthe computer readable program is executable by the one or moreprocessors to cause the system to perform operations including:presenting a graphical user interface (GUI) that includes multipleselectable segments that each define a different respective set ofsegment attributes; receiving input to the GUI selecting a particularsegment of the multiple selectable segments; generating a segment objectthat includes segment condition rules for the particular segment;communicating the segment object to a remote system; receiving a segmentdefinition that includes user statistics for a group of users inrelation to the particular segment and that does not include personalidentification information for the group of users; and communicating anoffer to be forwarded by the remote system to the group of users.
 2. Thesystem as recited in claim 1, wherein the segment attributes include oneor more of a gender identifier, an age range, or an income range for oneor more of the segments.
 3. The system as recited in claim 1, whereinthe segment object is not specific to the offer.
 4. The system asrecited in claim 1, wherein the segment condition rules includepredicates for user classification in the segment.
 5. The system asrecited in claim 1, wherein the segment condition rules include aminimum matching score for a user to be included in the particularsegment.
 6. The system as recited in claim 1, wherein the segmentcondition rules include a minimum matching score that specifies aminimum number of user attributes that are to match segment attributesof the particular segment for a user to be included in the particularsegment.
 7. The system as recited in claim 1, wherein the segmentcondition rules include a minimum matching score that specifies aminimum number of user attributes that are to match segment attributesof the particular segment for a user to be included in the particularsegment, the minimum matching score specifying less than all of thesegment attributes.
 8. The system as recited in claim 1, wherein thesegment condition rules specify a weighting for a particular consumerattribute of the particular segment, and wherein the weighting is to beapplied for matching a user to the particular segment.
 9. The system asrecited in claim 1, wherein the operations further include receivingoffer performance statistics that indicate a response of at least someusers of the group of users to the offer.
 10. A computer-implementedmethod, comprising: presenting a graphical user interface (GUI) thatincludes multiple selectable segments that each define a differentrespective set of segment attributes; receiving input to the GUIselecting a particular segment of the multiple selectable segments;generating a segment object that includes segment condition rules forthe particular segment; communicating the segment object to a remotesystem; receiving a segment definition that includes user statistics fora group of users in relation to the particular segment and that does notinclude personal identification information for the group of users; andcommunicating an offer to be forwarded by the remote system to the groupof users.
 11. The method as recited in claim 10, wherein the segmentcondition rules include predicates for user classification in theparticular segment.
 12. The method as recited in claim 10, wherein thesegment condition rules include a minimum matching score for a user tobe included in the particular segment.
 13. The method as recited inclaim 10, wherein the segment condition rules include a minimum matchingscore that specifies a minimum number of user attributes that are tomatch segment attributes of the particular segment for a user to beincluded in the particular segment.
 14. The method as recited in claim10, wherein the segment condition rules specify a weighting for aparticular consumer attribute of the particular segment, wherein theweighting is to be applied for matching a user to the segment.
 15. Themethod as recited in claim 10, further comprising receiving offerperformance statistics that indicate a response of at least some usersof the group of users to the offer.
 16. A computer-implemented method,comprising: receiving a segment object that defines a set of segmentattributes for a segment; comparing user attributes of a group of usersto segment rules for the segment to determine a match of the group ofusers to the segment; assigning a score to each user of the group ofusers based on a strength of a match of each user to the segment;placing the group of users into the segment based a score for each userof the group of users exceeding a threshold score for the segment;communicating a set of segment statistics for the group of usersassociated with the particular segment to an offer provider withoutpersonal identity information for the group of users; receiving an offerfrom the offer provider; and communicating the offer to the group ofusers.
 17. The method as recited in claim 16, wherein the segment rulesspecify segment attributes that are to be matched to user attributes fora user to be placed in the segment.
 18. The method as recited in claim16, wherein the segment statistics specify a number of users that areplaced in the segment.
 19. The method as recited in claim 16, whereinthe threshold score is based on a number of segment attributes includedin the segment object, and wherein the threshold score specifies anumber of segment attributes that is less than all segment attributesincluded in the segment object.
 20. The method as recited in claim 16,further comprising: determining performance statistics that indicate aresponse of at least some users of the group of users to the offer; andcommunicating the performance statistics to the offer provider.